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                        <h1 class="single-title flipInX">TensorFlow2.1入门学习笔记(14)——卷积神经网络InceptionNet, ResNet示例</h1><div class="post-meta summary-post-meta"><span class="post-category meta-item">
                                <a href="/categories/tf2.1%E5%AD%A6%E4%B9%A0%E7%AC%94%E8%AE%B0/"><span class="svg-icon icon-folder"></span>TF2.1学习笔记</a>
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                                <span class="svg-icon icon-clock"></span><time class="timeago" datetime="2020-06-19">2020-06-19</time>
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                                <span class="svg-icon icon-pencil"></span>约 2725 字
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                                <span class="svg-icon icon-stopwatch"></span>预计阅读 6 分钟
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  <ul>
    <li><a href="#inceptionnet">InceptionNet</a></li>
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                    </div><h2 id="inceptionnet" class="headerLink"><a href="#inceptionnet" class="header-mark"></a>InceptionNet</h2><p><a href="https://www.jianshu.com/p/6d66fa4ca9d7" target="_blank" rel="noopener noreffer">InceptionNet</a>诞生于2014年，当年ImageNet竞赛冠军，Top5错误率为6.67%
InceptionNet引入了Inception结构块，在同一层网络内使用不同尺寸的卷积核，提升了模型感知力使用了批标准化缓解了梯度消失</p>
<ul>
<li><a href="https://arxiv.org/pdf/1409.4842.pdf" target="_blank" rel="noopener noreffer">Inception V1</a>（GoogleNet）——构建了1x1、3x3、5x5的 conv 和3x3的 pooling 的分支网络module，同时使用MLPConv和全局平均池化，扩宽卷积层网络宽度，增加了网络对尺度的适应性；</li>
<li><a href="https://arxiv.org/pdf/1502.03167.pdf" target="_blank" rel="noopener noreffer">Inception V2</a>——提出了Batch Normalization，代替Dropout和LRN，其正则化的效果让大型卷积网络的训练速度加快很多倍，同时收敛后的分类准确率也可以得到大幅提高，同时借鉴VGGNet使用两个3x3的卷积核代替5x5的卷积核，在降低参数量同时提高网络学习能力；</li>
<li><a href="https://arxiv.org/pdf/1512.00567.pdf" target="_blank" rel="noopener noreffer">Inception V3</a>——引入了 Factorization，将一个较大的二维卷积拆成两个较小的一维卷积，比如将3x3卷积拆成1x3卷积和3x1卷积，一方面节约了大量参数，加速运算并减轻了过拟合，同时增加了一层非线性扩展模型表达能力，除了在 Inception Module 中使用分支，还在分支中使用了分支（Network In Network In Network）；</li>
<li><a href="https://arxiv.org/pdf/1602.07261.pdf" target="_blank" rel="noopener noreffer">Inception V4</a>——研究了 Inception Module 结合 Residual Connection，结合 ResNet 可以极大地加速训练，同时极大提升性能，在构建 Inception-ResNet 网络同时，还设计了一个更深更优化的 Inception v4 模型，能达到相媲美的性能。</li>
</ul>
<p><strong>网络结构</strong></p>
<p>InceptionNet的基本单位是Inception结构块，在同一层网络中使用了不同尺寸的卷积核，可以提取不同尺寸的特征信息
通过1x1卷积核作用到输入特征图的每个像素点，通过设定少于输入特征图的深度达到降维减少了参数量和计算量</p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200615134746533.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200615134746533.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>Inception结构块设计</strong></p>
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<pre class="chroma"><code class="language-python" data-lang="python"><span class="k">class</span> <span class="nc">ConvBNAct</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ConvBNAct</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
            <span class="n">Conv2D</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">),</span>
            <span class="n">BatchNormalization</span><span class="p">(),</span>
            <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
        <span class="p">])</span>
        
    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>

<span class="k">class</span> <span class="nc">InceptionBlk</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ch</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">InceptionBlk</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ch</span> <span class="o">=</span> <span class="n">ch</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">strides</span> <span class="o">=</span> <span class="n">strides</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c1</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c2_1</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c2_2</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c3_1</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c3_2</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">p4_1</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">p4_2</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x2_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c2_1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x2_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c2_2</span><span class="p">(</span><span class="n">x2_1</span><span class="p">)</span>
        <span class="n">x3_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c3_1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x3_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c3_2</span><span class="p">(</span><span class="n">x3_1</span><span class="p">)</span>
        <span class="n">x4_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p4_1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x4_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p4_2</span><span class="p">(</span><span class="n">x4_1</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2_2</span><span class="p">,</span> <span class="n">x3_2</span><span class="p">,</span> <span class="n">x4_2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>
</code></pre></td></tr></table>
</div>
</div><p><strong>精简InceptionNet</strong></p>
<p>包含四个Inception结构快，每两个结构块组成一个block，每个block的第一个结构块步长是2，使输出特征数据减半，第二个结构块步长是1，因此将输出特征图深度加深（self.out_channels *= 2），尽可能保证特征抽取信息的承载量一致</p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200617143000707.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
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            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>网络搭建示例</strong></p>
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<pre class="chroma"><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="kn">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">GlobalAveragePooling2D</span><span class="p">,</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPool2D</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">BatchNormalization</span><span class="p">,</span> <span class="n">Dropout</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">Model</span>

<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>

<span class="n">cifar10</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">cifar10</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">cifar10</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>

<span class="k">class</span> <span class="nc">ConvBNAct</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">ConvBNAct</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
            <span class="n">Conv2D</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">),</span>
            <span class="n">BatchNormalization</span><span class="p">(),</span>
            <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
        <span class="p">])</span>
        
    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">model</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">training</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>




<span class="k">class</span> <span class="nc">InceptionBlk</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ch</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">InceptionBlk</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">ch</span> <span class="o">=</span> <span class="n">ch</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">strides</span> <span class="o">=</span> <span class="n">strides</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c1</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c2_1</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c2_2</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c3_1</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c3_2</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">p4_1</span> <span class="o">=</span> <span class="n">MaxPool2D</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">p4_2</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">ch</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x2_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c2_1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x2_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c2_2</span><span class="p">(</span><span class="n">x2_1</span><span class="p">)</span>
        <span class="n">x3_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c3_1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x3_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c3_2</span><span class="p">(</span><span class="n">x3_1</span><span class="p">)</span>
        <span class="n">x4_1</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p4_1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x4_2</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p4_2</span><span class="p">(</span><span class="n">x4_1</span><span class="p">)</span>

        <span class="n">x</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">([</span><span class="n">x1</span><span class="p">,</span> <span class="n">x2_2</span><span class="p">,</span> <span class="n">x3_2</span><span class="p">,</span> <span class="n">x4_2</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">x</span>


<span class="k">class</span> <span class="nc">Inception10</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">num_blocks</span><span class="p">,</span> <span class="n">num_classes</span><span class="p">,</span> <span class="n">init_ch</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
        <span class="nb">super</span><span class="p">(</span><span class="n">Inception10</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">in_channels</span> <span class="o">=</span> <span class="n">init_ch</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">out_channels</span> <span class="o">=</span> <span class="n">init_ch</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">num_blocks</span> <span class="o">=</span> <span class="n">num_blocks</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">init_ch</span> <span class="o">=</span> <span class="n">init_ch</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">c1</span> <span class="o">=</span> <span class="n">ConvBNAct</span><span class="p">(</span><span class="n">init_ch</span><span class="p">)</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">blocks</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
        <span class="k">for</span> <span class="n">block_id</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">num_blocks</span><span class="p">):</span>
            <span class="k">for</span> <span class="n">layer_id</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
                <span class="k">if</span> <span class="n">layer_id</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
                    <span class="n">block</span> <span class="o">=</span> <span class="n">InceptionBlk</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">out_channels</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
                <span class="k">else</span><span class="p">:</span>
                    <span class="n">block</span> <span class="o">=</span> <span class="n">InceptionBlk</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">out_channels</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
                <span class="bp">self</span><span class="o">.</span><span class="n">blocks</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</span><span class="p">)</span>

            <span class="bp">self</span><span class="o">.</span><span class="n">out_channels</span> <span class="o">*=</span> <span class="mi">2</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">p1</span> <span class="o">=</span> <span class="n">GlobalAveragePooling2D</span><span class="p">()</span>
        <span class="bp">self</span><span class="o">.</span><span class="n">f1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="n">num_classes</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">)</span>

    <span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">x</span><span class="p">):</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocks</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
        <span class="k">return</span> <span class="n">y</span>

<span class="n">model</span> <span class="o">=</span> <span class="n">Inception10</span><span class="p">(</span><span class="n">num_blocks</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>

<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span>
              <span class="n">loss</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
              <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">])</span>

<span class="n">checkpoint_save_path</span> <span class="o">=</span> <span class="s1">&#39;./checkpoint/Inception10.ckpt&#39;</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">checkpoint_save_path</span> <span class="o">+</span> <span class="s2">&#34;.index&#34;</span><span class="p">):</span>
    <span class="k">print</span><span class="p">(</span><span class="s1">&#39;------------- load the model ------------&#39;</span><span class="p">)</span>
    <span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="n">checkpoint_save_path</span><span class="p">)</span>

<span class="n">cp_callback</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="n">checkpoint_save_path</span><span class="p">,</span>
                                                 <span class="n">save_weights_only</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
                                                 <span class="n">save_best_only</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span>
					<span class="n">validation_freq</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">cp_callback</span><span class="p">])</span>

<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="nb">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;./weights.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">trainable_variables</span><span class="p">:</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
    <span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>

<span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
<span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></td></tr></table>
</div>
</div><p><strong>运行结果</strong></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200617211935483.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200617211935483.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200617212519939.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200617212519939.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200617212544639.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200617212544639.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<h2 id="resnet" class="headerLink"><a href="#resnet" class="header-mark"></a>ResNet</h2><p><a href="https://zhuanlan.zhihu.com/p/31852747" target="_blank" rel="noopener noreffer">ResNet</a>诞生于2015年，当年ImageNet竞赛冠军，Top5错误率为3.57%</p>
<p>网络的深度对模型的性能至关重要，当增加网络层数后，网络可以进行更加复杂的特征模式的提取，所以当模型更深时理论上可以取得更好的结果，从前面的网络可以看出网络越深而效果越好的一个实践证据。但是实验发现深度网络出现了退化问题（Degradation problem）：网络深度增加时，网络准确度出现饱和，甚至出现下降。这个现象可以在下图中直观看出来：56层的网络比20层网络效果还要差。这不会是过拟合问题，因为56层网络的训练误差同样高。我们知道深层网络存在着梯度消失或者爆炸的问题，这使得深度学习模型很难训练。但是现在已经存在一些技术手段如BatchNorm来缓解这个问题。</p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200618234355214.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200618234355214.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure>
假设现在有一个浅层网络，想通过向上堆积新层来建立深层网络，一个极端情况是这些增加的层什么也不学习，仅仅复制浅层网络的特征，即这样新层是恒等映射（Identity mapping）。在这种情况下，深层网络应该至少和浅层网络性能一样，也不应该出现退化现象。因此不得不承认肯定是目前的训练方法有问题，才使得深层网络很难去找到一个好的参数。</p>
<p>何凯明由此提出了残差学习来解决退化问题。对于一个堆积层结构（几层堆积而成）当输入为$x$时其学习到的特征记为 $H(x)$ ，现在我们希望其可以学习到残差 $F(x) = H(x) - x$ ，这样其实原始的学习特征是 $F(x) + x$ 。之所以这样是因为残差学习相比原始特征直接学习更容易。当残差为0时，此时堆积层仅仅做了恒等映射，至少网络性能不会下降，实际上残差不会为0，这也会使得堆积层在输入特征基础上学习到新的特征，从而拥有更好的性能。残差学习的结构如图4所示。这有点类似与电路中的“短路”，所以是一种短路连接（shortcut connection）。</p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200618235949168.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200618235949168.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>ResNet18的网络结构</strong></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200618235842459.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200618235842459.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p><strong>网络搭建示例</strong></p>
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<td class="lntd">
<pre class="chroma"><code class="language-python" data-lang="python"><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="kn">as</span> <span class="nn">tf</span> 
<span class="kn">import</span> <span class="nn">numpy</span> <span class="kn">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">os</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras.layers</span> <span class="kn">import</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">Dense</span><span class="p">,</span> <span class="n">Activation</span><span class="p">,</span> <span class="n">MaxPool2D</span><span class="p">,</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">BatchNormalization</span>
<span class="kn">from</span> <span class="nn">tensorflow.keras</span> <span class="kn">import</span> <span class="n">Model</span>
<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">pyplot</span> <span class="k">as</span> <span class="n">plt</span> 

<span class="n">np</span><span class="o">.</span><span class="n">set_printoptions</span><span class="p">(</span><span class="n">threshold</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">inf</span><span class="p">)</span>

<span class="n">cifar10</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">cifar10</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">cifar10</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>


<span class="k">class</span> <span class="nc">ResBlock</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>

	<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filters</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">residual_path</span><span class="o">=</span><span class="bp">False</span><span class="p">):</span>
		<span class="nb">super</span><span class="p">(</span><span class="n">ResBlock</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">filters</span> <span class="o">=</span> <span class="n">filters</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">strides</span> <span class="o">=</span> <span class="n">strides</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">residual_path</span> <span class="o">=</span> <span class="n">residual_path</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c1</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b1</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a1</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c2</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b2</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>

		<span class="k">if</span> <span class="n">residual_path</span><span class="p">:</span>
			<span class="bp">self</span><span class="o">.</span><span class="n">down_c1</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="p">,</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="n">strides</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
			<span class="bp">self</span><span class="o">.</span><span class="n">down_b1</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">a2</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>

	<span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
		<span class="n">residual</span> <span class="o">=</span> <span class="n">inputs</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b2</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>

		<span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">residual_path</span><span class="p">:</span>
			<span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">down_c1</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
			<span class="n">residual</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">down_b1</span><span class="p">(</span><span class="n">residual</span><span class="p">)</span>
		
		<span class="n">out</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a2</span><span class="p">(</span><span class="n">y</span> <span class="o">+</span> <span class="n">residual</span><span class="p">)</span>
		<span class="k">return</span> <span class="n">out</span>

<span class="k">class</span> <span class="nc">ResNet</span><span class="p">(</span><span class="n">Model</span><span class="p">):</span>
	<span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">block_list</span><span class="p">,</span> <span class="n">initial_filters</span><span class="o">=</span><span class="mi">64</span><span class="p">):</span>
		<span class="nb">super</span><span class="p">(</span><span class="n">ResNet</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">num_blocks</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">block_list</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">block_list</span> <span class="o">=</span> <span class="n">block_list</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">out_filters</span> <span class="o">=</span> <span class="n">initial_filters</span>

		<span class="bp">self</span><span class="o">.</span><span class="n">c1</span> <span class="o">=</span> <span class="n">Conv2D</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">out_filters</span><span class="p">,</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="n">strides</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">&#39;same&#39;</span><span class="p">,</span> <span class="n">use_bias</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">b1</span> <span class="o">=</span> <span class="n">BatchNormalization</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">a1</span> <span class="o">=</span> <span class="n">Activation</span><span class="p">(</span><span class="s1">&#39;relu&#39;</span><span class="p">)</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">blocks</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">Sequential</span><span class="p">()</span>
		<span class="k">for</span> <span class="n">block_id</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">block_list</span><span class="p">)):</span>
			<span class="k">for</span> <span class="n">layers_id</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">block_list</span><span class="p">[</span><span class="n">block_id</span><span class="p">]):</span>
				<span class="k">if</span> <span class="n">block_list</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="n">layers_id</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
					<span class="n">block</span> <span class="o">=</span> <span class="n">ResBlock</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">out_filters</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">residual_path</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>
				<span class="k">else</span><span class="p">:</span>
					<span class="n">block</span> <span class="o">=</span> <span class="n">ResBlock</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">out_filters</span><span class="p">,</span> <span class="n">residual_path</span><span class="o">=</span><span class="bp">False</span><span class="p">)</span>
				<span class="bp">self</span><span class="o">.</span><span class="n">blocks</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">block</span><span class="p">)</span>
			<span class="bp">self</span><span class="o">.</span><span class="n">out_filters</span> <span class="o">*=</span> <span class="mi">2</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">p1</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">AveragePooling2D</span><span class="p">()</span>
		<span class="bp">self</span><span class="o">.</span><span class="n">f1</span> <span class="o">=</span> <span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">&#39;softmax&#39;</span><span class="p">,</span> <span class="n">kernel_regularizer</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">regularizers</span><span class="o">.</span><span class="n">l2</span><span class="p">())</span>

	<span class="k">def</span> <span class="nf">call</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">inputs</span><span class="p">):</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">c1</span><span class="p">(</span><span class="n">inputs</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">b1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">a1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">blocks</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">x</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">p1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="n">y</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">f1</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
		<span class="k">return</span> <span class="n">y</span>

<span class="k">def</span> <span class="nf">Resnet18</span><span class="p">():</span>
	<span class="k">return</span> <span class="n">ResNet</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">Resnet34</span><span class="p">():</span>
    <span class="k">return</span> <span class="n">ResNet</span><span class="p">([</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">6</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
	

<span class="n">model</span> <span class="o">=</span> <span class="n">Resnet18</span><span class="p">()</span>

<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">&#39;adam&#39;</span><span class="p">,</span>
				<span class="n">loss</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="bp">False</span><span class="p">),</span>
				<span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">])</span>

<span class="n">checkpoint_save_path</span> <span class="o">=</span> <span class="s1">&#39;./checkpoint/ResNet18.ckpt&#39;</span>
<span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">exists</span><span class="p">(</span><span class="n">checkpoint_save_path</span> <span class="o">+</span> <span class="s1">&#39;.index&#39;</span><span class="p">):</span>
	<span class="k">print</span><span class="p">(</span><span class="s1">&#39;--------------- load the model -----------------&#39;</span><span class="p">)</span>
	<span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="n">checkpoint_save_path</span><span class="p">)</span>

<span class="n">cp_callback</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">callbacks</span><span class="o">.</span><span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="n">checkpoint_save_path</span><span class="p">,</span>
												<span class="n">save_weights_only</span><span class="o">=</span><span class="bp">True</span><span class="p">,</span>
												<span class="n">save_best_only</span><span class="o">=</span><span class="bp">True</span><span class="p">)</span>

<span class="n">history</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">),</span> <span class="n">validation_freq</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                    <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">cp_callback</span><span class="p">])</span>


<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span>

<span class="nb">file</span> <span class="o">=</span> <span class="nb">open</span><span class="p">(</span><span class="s1">&#39;./weights.txt&#39;</span><span class="p">,</span> <span class="s1">&#39;w&#39;</span><span class="p">)</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">trainable_variables</span><span class="p">:</span>
	<span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">name</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
	<span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
	<span class="nb">file</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">v</span><span class="o">.</span><span class="n">numpy</span><span class="p">())</span> <span class="o">+</span> <span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
<span class="nb">file</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>

<span class="n">acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">val_acc</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_sparse_categorical_accuracy&#39;</span><span class="p">]</span>
<span class="n">loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;loss&#39;</span><span class="p">]</span>
<span class="n">val_loss</span> <span class="o">=</span> <span class="n">history</span><span class="o">.</span><span class="n">history</span><span class="p">[</span><span class="s1">&#39;val_loss&#39;</span><span class="p">]</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_acc</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Accuracy&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>

<span class="n">plt</span><span class="o">.</span><span class="n">subplot</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Training Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">val_loss</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Training and Validation Loss&#39;</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
</code></pre></td></tr></table>
</div>
</div><p><strong>运行结果</strong>





<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/2020061823512060.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/2020061823512060.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
<p>




<figure class="render-image"><a target="_blank" href="https://img-blog.csdnimg.cn/20200618235007173.png" title=" " >
        <img loading="lazy" decoding="async"
             class="render-image"
             src="https://img-blog.csdnimg.cn/20200618235007173.png"
            alt=" "
        />
    </a><figcaption class="image-caption"> </figcaption>
</figure></p>
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